Europe's AI catch-up: A domestic AI industry with the “Apply AI strategy” – Between sovereignty and competitive reality

1. A late course correction in turbulent times

The European Union is at a critical turning point in its digital history. While it has for years acted as a regulatory pioneer in the field of artificial intelligence, there is a growing realization that a purely regulatory approach is insufficient to compete in the global AI race. In October 2025, the European Commission presented its new “Apply AI Strategy,” which marks a fundamental paradigm shift: Instead of just regulating, Europe now finally wants to take the initiative and build its own AI industry.

The strategy comes at a time when Europe's dependence on American and Chinese technologies has reached dramatic proportions. More than three-quarters of listed European companies rely on US cloud services, and Europe lags significantly behind in critical AI technologies. While the US holds the top position in quantum computing and artificial intelligence, and China is rapidly catching up in semiconductors, Europe ranks a distant third in all three key technologies.

The new strategy builds on a painful realization: Europe largely missed the digital revolution of the past two decades and now risks falling behind in AI as well. With one billion euros from existing programs, the European Commission aims to promote the use of European AI solutions in eight strategic sectors – from healthcare and energy to defense and the automotive industry. The stated goal is to move beyond the position of a “digital colony” and achieve technological sovereignty.

2. From regulator to laggard: Europe's digital odyssey

The origins of Europe's AI strategy date back to the early days of digital transformation. As early as the 2000s, the EU recognized the importance of digital technologies but focused primarily on establishing legal frameworks. While American companies like Google, Amazon, and Microsoft expanded their market dominance and Chinese corporations like Alibaba and Tencent grew rapidly, Europe prioritized regulation and data protection.

The decisive turning point came with the adoption of the General Data Protection Regulation (GDPR) in 2018, which established Europe as a global standard-setter. This success story was repeated with the AI ​​Act, which entered into force in 2024 as the world's first comprehensive AI law. The AI ​​Act's risk-based approach classifies AI systems into different categories and subjects high-risk applications to strict requirements – from transparency obligations to human oversight.

But the focus on regulation came at a price. While Europe was writing laws, other continents were building companies. The Mario Draghi report of September 2024 bluntly summarized this predicament: Europe needed to become significantly more innovative to compete with the US and China. The EU was trapped in a static industrial structure where only a few new companies emerged to transform existing industries or develop new engines of growth.

The figures speak for themselves: Only four of the world's 50 largest technology companies are European. When it comes to AI investments, 61 percent of the world's machine learning models rated as "remarkable" originate in the US, followed by the EU with 21 percent and China with 15 percent. In 2024, the EU invested only 6 percent of the more than $35 billion in global AI startup funding. These sobering realities have led to a change of thinking in Brussels: Regulation alone is not enough – Europe needs an industrial policy response to the AI ​​challenge.

3. The building blocks of the new AI offensive

The EU's new Apply AI strategy rests on several strategic pillars designed to create a coherent ecosystem for European AI innovation. At its core is the transformation of the existing 151 European Digital Innovation Hubs (EDIHs) into specialized "AI experience centers." These hubs will provide small and medium-sized enterprises (SMEs) with privileged access to the EU's AI innovation ecosystem and help bridge the digital divide between large corporations and SMEs.

The core of the technical infrastructure consists of the AI ​​factories and planned gigafactories. The EU has already selected 19 locations for AI factories and plans six more in the Czech Republic, Lithuania, Poland, Romania, Spain, and the Netherlands. These facilities are intended to offer startups, SMEs, and industry direct access to AI-optimized supercomputers. Investments amount to over €500 million for the new locations alone, while the more ambitious gigafactories are planned with a total investment of €20 billion.

A key component is the newly created Apply AI Alliance, a coordination forum bringing together industry, the public sector, academia, social partners, and civil society. This alliance will act as a central interface between AI stakeholders and the Commission, driving dialogue on AI policy in strategic EU sectors. In parallel, the Frontier AI initiative will be launched, uniting Europe's leading industrial and academic players to accelerate progress in frontier AI capabilities.

The strategy identifies eight priority sectors for AI implementation: healthcare and pharmaceuticals, mobility and transport, robotics, manufacturing and engineering, climate and environment, energy, agriculture and food, and defense and security. Particularly in healthcare, the EU is focusing on concrete applications such as AI-supported screening centers, which are intended to enable more accurate diagnoses using imaging techniques. In science, the virtual European institute RAISE is being created to pool AI resources for the development and application of AI in research.

4. Between Ambitions and Realities

The implementation of the Apply AI strategy is taking place in a challenging environment characterized by geopolitical tensions and technological dependencies. Currently, only 37 percent of German companies use AI technologies, with large companies being significantly more active at 66 percent compared to small businesses at 36 percent. Across Europe, AI adoption stands at just 13.5 percent of companies, while the EU aims for a rate of 75 percent by 2030.

The biggest challenge lies in the structural dependence on foreign technologies. Around 75 percent of European companies rely on American cloud providers, and US and Asian suppliers dominate the market for critical AI components such as semiconductors and AI chips. This dependence is exacerbated by geopolitical developments: The Trump administration, with its AI Action Plan strategy, set the goal of achieving “global technological dominance” and making allies structurally dependent on US technology.

Europe faces the dilemma of implementing its AI strategy in a market already dominated by others. Even promising European AI companies like the French firm Mistral rely on foreign suppliers for hardware, software, and critical minerals. Mistral, considered a beacon of hope for European Large Language Models, is valued at nearly twelve billion euros, while direct US competitors like OpenAI, Anthropic, and xAI are valued at hundreds of billions of dollars.

The regulatory frameworks that Europe markets as a strength are increasingly perceived by industry as a hindrance to innovation. Critics describe the AI ​​Act as a “bureaucratic monster” that imposes particularly high compliance costs on small and medium-sized enterprises. Legal experts speak of an “absolute compliance overkill” for high-risk AI applications, which could stifle innovation. This criticism is reinforced by the fact that only eleven percent of the 383 recommendations from the Draghi report have been implemented so far.

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5. Success stories and learning examples from practice

Despite the structural challenges, there are already remarkable examples of successful AI implementation in European companies, demonstrating what is possible when the right framework is in place. Siemens AG has transformed its Digital Lighthouse factory in Erlangen into a prime example of industrial AI applications. By using AI, digital twins, and robotics in over 100 use cases, the company achieved a 69 percent increase in productivity and 42 percent energy savings over four years.

Another impressive example is Zalando, which offers 29 million customers a personalized shopping experience with its “Algorithmic Fashion Companion.” This digital outfit recommendation tool is based on AI and machine learning and optimizes not only the customer experience but also internal processes such as supply chains and fraud prevention. Particularly during Cyber ​​Week, AI enables the company to create an exemplary customer experience with flexible payment and delivery options, as well as week-specific discounts.

In the realm of smaller companies, the example of Kaput Podcasts demonstrates how generative AI technologies can revolutionize creative processes. The company was able to reduce the time spent on repetitive tasks in podcast production by 75 percent without compromising quality. This case study highlights the enormous potential of AI for content creation and media production – areas where Europe has traditionally excelled.

These success stories also highlight the strategic advantages that Europe can leverage in the AI ​​race. Unlike purely software-based applications, Europe possesses deep industrial know-how and high-quality domain data. By embedding this expertise in AI-powered applications in areas such as operations, procurement, and finance, European complexity can be transformed into European competitiveness. Particularly with tabular models trained on structured data, manufacturers can use their data efficiently—a particularly valuable advantage where verifiability is essential.

6. Structural obstacles and systemic weaknesses

The implementation of the European AI strategy is hampered by a number of structural problems deeply rooted in the DNA of the European innovation ecosystem. The most serious problem is the lack of complementary markets necessary for a successful AI business. Europe lacks large commercial customers for frontier generative AI models that could generate sufficient revenue to cover the enormous fixed costs of model training. Likewise, hyperscale cloud computing infrastructures and private equity funding for AI startups on a European scale are lacking.

The costs of catching up with leading Big Tech AI computing centers are already prohibitive for EU budgets and are expected to rise further. While the EU is focusing on expanding an existing supercomputing network with more AI hardware, this computing infrastructure is not suited for AI modeling. This hardware focus overlooks the lack of EU markets for complementary services needed to build a successful AI business.

Another systemic problem lies in the fragmented structure of the European single market. Despite theoretical harmonization, companies still have to contend with differing national implementations and bureaucratic hurdles in practice. This fragmentation is further exacerbated by the AI ​​Act, as different member states can develop different interpretations of the regulations. Double regulations under the AI ​​Act, the Data Protection Act, and the GDPR create additional complexity that can be particularly overwhelming for smaller companies.

The EU-US trade agreement further entrenches dependence on foreign technologies. While Europe imports over €300 billion worth of digital services annually from the US, the EU continues to fail to impose a uniform digital tax on the revenues of US tech giants in the European market. At the same time, the agreement diverts significant investments from Europe to US industry, at the expense of building European capacity. The situation is exacerbated by the erratic policies of the Trump administration, which treats Europe as a potential “data colony” and seeks to promote digital imperialism through the export of the entire American AI stack.

7. Scenarios for the European AI future

The future of the European AI strategy depends on various factors, which could manifest in different scenarios. In the most optimistic scenario, Europe succeeds in combining its industrial expertise and regulatory competence to create a unique market position. The “Trusted AI” model could establish itself as a global standard, similar to how the GDPR influenced worldwide data protection regulations. In this scenario, European AI solutions would be marketed as particularly trustworthy and ethical, granting them access to sensitive sectors such as healthcare and financial services.

A more likely middle scenario sees Europe as a successful “application world champion,” not competing in frontier models but becoming a leader in specialized industrial AI applications. In this model, Europe focuses on AI applications below the technology frontier, requiring far less computing power and lower investment costs. By promoting the adoption of AI application services across a broad range of industries, Europe could significantly stimulate productivity growth without entering the prohibitively expensive frontier race.

The more pessimistic scenario sees Europe as a permanent laggard, remaining structurally dependent on American and Chinese technologies. The three global AI strategies—the US frontier race, Europe's regulatory path, and China's ground-based applications—could develop in such a way that Europe is caught in the middle. While the US maintains its technological lead through private investment and daring innovation, and China maximizes practical benefits through state-coordinated mass deployment, Europe's regulatory approach could hinder both innovation and adoption.

Geopolitical developments will be crucial. If the US and China settle into a new technological Cold War, Europe could be forced to choose a side or attempt to maintain a neutral position. A “balance between great powers” ​​could certainly benefit Europe if it skillfully navigates between the opposing camps while expanding its own technological niches. Alternatively, Europe could also try to form a “middle-power alliance” with countries like India, Japan, or South Korea to jointly confront the great powers.

8. Turning Point or Seeming Turn: A Critical Assessment

The European Union's Apply AI strategy undoubtedly marks a significant turning point in European technology policy. After years of a primarily regulatory approach, the strategy signals a willingness to finally act as a technological player. The billion euros in funding, the establishment of AI factories, and the transformation of digital innovation hubs demonstrate that Europe has recognized its responsibilities.

Nevertheless, considerable doubts remain as to whether these measures will be sufficient to overcome the structural deficits. The financial allocation of one billion euros seems modest compared to the 58.5 billion dollars that the US alone invested in AI venture capital in 2024. Even the more ambitious 20 billion euros for the gigafactories is a fraction of what would be necessary for a genuine catch-up. Mario Draghi's demand for additional annual investments of 750 to 800 billion euros makes clear the scale on which Europe would have to think.

The biggest challenge lies not in the technology itself, but in market structures and business models. As long as European AI startups are forced to cooperate with US Big Tech companies to gain access to computing power, data, and markets, this dependency will persist. The Apply AI strategy only superficially addresses these fundamental problems and relies too heavily on government intervention in a field primarily driven by private innovation and venture capital.

Europe's best chance may not lie in direct competition with the US and China for frontier AI, but rather in skillfully leveraging its specific strengths. The combination of industrial expertise, high-quality data, and trustworthy regulation could create a unique market position. If Europe succeeds in making AI a natural tool in its traditional areas of strength – from mechanical engineering and the chemical industry to the automotive sector – it could find a profitable niche in the global AI ecosystem.

The Apply AI strategy is a necessary but not sufficient step. It shows that Europe understands the challenge, but leaves open the question of whether the political will and financial resources are sufficient to turn the vision into reality. The window of opportunity for a successful European AI strategy is closing rapidly – ​​but it is not completely closed yet.

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